(2) Extended Experiments. In order to further validate our algorithm, we also report results on COCO minival dataset compared with other detectors. We use a COCO training setting which is the same as [1]} in the batch size, frozen Batch Normalization, learning rate, etc. For ablation studies, we use ResNet-50 backbone and run 135 iterations of training. We notice that the performance of the proposed label assignment is improved by adding it to other widely-used frameworks, such as 3.6% on FPN [2]}, 5.2% on RetinaNet [3]} and 4.8% on FCOS [4]}. Furthermore, compared with other label assignment strategies, ours also show competitive performance in Table REF . Although our algorithm is designed specially for pedestrian detection, it still has some performance improvement for more general detection dataset such as COCO. We believe that the performance will be further improved if there is a more refined design for the general purpose.
